Document mining using graph neural network

被引：0

作者：

Yong, S. L. ^{[1
]}

Hagenbuchner, M. ^{[1
]}

Tsoi, A. C. ^{[2
]}

Scarselli, F. ^{[3
]}

Gori, M. ^{[3
]}

机构：

[1] Univ Wollongong, Wollongong, NSW 2500, Australia

[2] Monash Univ, Melbourne, Vic 3800, Australia

[3] Univ Siena, Siena, Italy

来源：

COMPARATIVE EVALUATION OF XML INFORMATION RETRIEVAL SYSTEMS | 2007年 / 4518卷

基金：

澳大利亚研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Graph Neural Network is a relatively new machine learning method capable of encoding data as well as relationships between data elements. This paper applies the Graph Neural Network for the first time to a given learning task at an international competition on the classification of semi-structured documents. Within this setting, the Graph Neural Network is trained to encode and process a relatively large set of XML formatted documents. It will be shown that the performance using the Graph Neural Network approach significantly outperforms the results submitted by the best competitor.

引用

页码：458 / 472

页数：15

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